Efficient deep learning models have been recently proposed to address this issue. A neural network formulation of the Bag-of-Features model was used in [1]} and was shown to perform on par with standard deep learning models. Methods extending the Neural-Bag-of-Features model to exploit the temporal domain of the time-series data, as well as long- and short-term information were proposed in [2]}, [3]}, [4]}. Based on the fact that time-series data can be encoded as tensors, the Temporal Attention-Augmented Bilinear (TABL) network was proposed in [5]}. TABL network was shown to perform on par with state-of-the-art deep learning models in stock mid-price direction prediction, while being much more efficient compared to the rest of the models.
